In medical diagnosis and treatment procedures, it is often necessary for medical staff to delineate objects on a medical image of a patient. For example, in radiotherapy treatment a radiation oncologist is normally required to delineate a tumour and target volume on a medical image of the patient for radiotherapy planning. The medical image is normally a computed tomography (CT) scan, although it may be another scanning modality image type such as a magnetic resonance (MR) scan, Positron Emission Tomography (PET) scan, etc. It is also often necessary to contour ‘normal’ (i.e. healthy) organs, considered organs at risk on the planning image. This is done so that radiotherapy planning software, which (semi-)automatically calculates a treatment plan, can maximise the planned radiation dose to the target volume and tumour, while minimising radiation dose to healthy organs.
A medical scan provides a ‘dataset’. The dataset comprises digital information about the value of a variable at each of many spatial locations in either a two-dimensional or (more typically) a three-dimensional space. In the case of a 3D volumetric dataset, the scan image is typically made up of a stack of 2D cross-sectional images. It is to be understood that the term ‘image’ used herein may refer to either a three-dimensional volumetric image dataset or a two-dimensional planar image dataset, unless otherwise stated or as may be apparent from the context within which the term is used.
The process of delineation is known as contouring. Typically, contours are drawn in two dimensions (2D) on cross-sectional images, although the planning medical image is typically a three dimensional (3D) volumetric image. As such, the delineation of an object such as a tumour, organ, etc. would consist of a stack of 2D contours, representing a volumetric outline of the object being delineated. Contouring can also be performed in 3D using some systems. Since delineation can be both 2D and 3D, the term ‘structure’ is used to represent the delineation of an object, whether 2D or 3D. Where the term contour is used, this will indicate a single 2D delineation only. However, the term contouring may indicate the process of delineation in either 2D or 3D to form a structure.
Manual contouring, as generally performed for radiotherapy contouring, is time consuming and is one of the bottlenecks in the treatment planning process, particularly for advanced treatments needing detailed contouring. Accordingly, systems have been developed for generating structures automatically, known as auto-contouring.
One approach to auto-contouring is atlas-based auto-contouring. An atlas consists of a medical image, normally a CT scan, with one or more structures that have previously been delineated by an expert. On receiving a new (patient) medical image to be delineated, the auto-contouring system would align the atlas to the patient image using a process known as image registration. The contours from the atlas would then be mapped onto the patient image using the calculated registration. The auto-contouring system would then return the mapped structures as a starting point for manual contouring of the patient image, whereby an expert (e.g. an radiation oncologist) is able edit the mapped structures to ensure the accuracy of the delineation.
Auto-contouring systems can help to reduce the amount of time a user is required to spend delineating a patient image. However, the accuracy of the auto-contouring has a significant impact on the amount of editing of the mapped structure(s) a user is required to perform, and thus the effectiveness of the auto-contouring systems in reducing the amount of time a user is required to spend delineating a patient image.
Two approaches have been demonstrated to improve atlas-based auto-contouring, both rather ambiguously referred to as multi-atlas auto-contouring.
For the first of these two multi-atlas auto-contouring approaches (herein after referred to as ‘multi-atlas fusion auto-contouring’), multiple atlases are registered to the patient image, and their respective structures mapped onto the patient image. This results in multiple structures being mapped onto the patient image for an object being delineated. The multiple structures are then merged, or ‘fused’, for each object into a consensus structure.
For the second of these two multi-atlas auto-contouring approaches (herein after referred to as ‘multi-atlas selection auto-contouring’), the patent image is compared to multiple atlases and the ‘most similar’ atlas to the patient image is selected. Selection methods include similarity measures between the registered images (e.g. mutual information) and similarity between meta-data of the images (e.g. patient age and sex). The selected atlas is then used to auto-contour the patient image.
These two approaches may also be combined such that a subset of atlases from the full set of atlases are selected based on their similarity to the patient image. The selected subset of atlases may then be used to auto-contour the patient image using the multi-atlas fusion auto-contouring approach.
Such multi-atlas auto-contouring approaches have been widely published, for example in:                (i) Han, Xiao, et al. “Atlas-based auto-segmentation of head and neck CT images.” Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008. Springer Berlin Heidelberg, 2008. 434-441.        (ii) Ramus, Liliane, and Grégoire Malandain. “Multi-atlas based segmentation: Application to the head and neck region for radiotherapy planning.” MICCAI Workshop Medical Image Analysis for the Clinic-A Grand Challenge. 2010.        (iii) Aljabar, Paul, et al. “Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy.” Neuroimage 46.3 (2009): 726-738.        (iv) Rohlfing, Torsten, et al. “Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains.” NeuroImage 21.4 (2004): 1428-1442.        
Over time, more and more medical images become (expertly) delineated and thus become available as atlases for use in auto-contouring systems. Advantageously, the greater the ‘pool’ of available atlases for auto-contouring, the greater the potential accuracy of the multi-atlas auto-contouring systems. However, the use of large atlas databases during auto-contouring has a number of drawbacks for clinical implementation:                1. The atlas database must be distributed to, and stored at, each location (e.g. terminal/computer) where the auto-contouring system is being used—the larger the database the more data that must be distributed and stored; and        2. The larger the database, the greater the computational burden for the auto-contouring process.        
Neither the distribution/storing of a large atlas database, nor the computational burden of processing a large atlas database as part of a multi-atlas auto-contouring process scale well as the database size increases. Accordingly, for practical clinical implementations it is necessary to restrict the size of the atlas databases used by a multi-atlas auto-contouring systems to, say, less than twenty of the ‘best’ atlases.
Thus, a challenge arises of how to select (offline) the best five or ten atlases from the larger pool of potentially hundreds or even thousands of available atlases for distribution to and clinical use (online) by the auto-contouring systems.
U.S. Pat. No. 8,411,950 discloses a proposed method for such an offline selection of a subset of M atlases from a larger group (pool) of N candidate atlases, and the subsequent use of the selected subset of M atlases in a multi-atlas selection auto-contouring system. However, the proposed method of U.S. Pat. No. 8,411,950 suffers from a number of drawbacks:                1. The offline selection method taught in U.S. Pat. No. 8,411,950 is specifically targeted at evaluating the performance of subsets of atlases in relation to the specific multi-atlas selection auto-contouring method disclosed therein. In particular, the offline selection method taught in U.S. Pat. No. 8,411,950 involves a calculating a regression to a similarity measure used in the target multi-atlas selection auto-contouring method. Changing the similarity measured used in the multi-atlas selection auto-contouring method would require re-running the selection process for the new similarity measure. As such, the selected subset of M atlases is only applicable to multi-atlas auto-contouring systems that utilise the specific multi-atlas selection auto-contouring method at which the selection process is targeted.        2. The offline selection method taught in U.S. Pat. No. 8,411,950 consists of a combinatorial search of every combination of subset of M atlases. For example, selecting a subset of 10 atlases from a pool of, say, 60 candidate atlases would require 75,394 million subset combinations to be assessed. Even if the evaluation process for each combination took 1 millisecond, this would require over two years of computation. Noting that in practice a pool of candidate atlases could consists of hundreds or even thousands of atlases, such a computationally intensive method is not feasible in practice. Furthermore, as the pool of candidate atlases is added and evolves/grows over time, the selection of a subset of ‘best’ atlases should be performed periodically to take into account new candidate atlases added to the larger pool.        3. The offline selection method taught in U.S. Pat. No. 8,411,950 assumes a single delineation structure per atlas. In clinical practice, many delineation structures representing different regions or organs may be present on each atlas. How the offline selection method taught in U.S. Pat. No. 8,411,950 could be adapted for atlases with multiple structures is not apparent.        
Thus, there is a need for an improved technique for the offline selection of a subset of atlases from a larger pool of candidate atlases, for distribution to and clinical use (online) by multi-atlas auto-contouring systems.